mapreduce.jobtracker.jobhistory.location If job tracker is static the history files are stored
in this single well known place. If No value is set here, by default,
it is in the local file system at ${hadoop.log.dir}/history.
mapreduce.jobtracker.jobhistory.task.numberprogresssplits12 Every task attempt progresses from 0.0 to 1.0 [unless
it fails or is killed]. We record, for each task attempt, certain
statistics over each twelfth of the progress range. You can change
the number of intervals we divide the entire range of progress into
by setting this property. Higher values give more precision to the
recorded data, but costs more memory in the job tracker at runtime.
Each increment in this attribute costs 16 bytes per running task.
mapreduce.job.userhistorylocation User can specify a location to store the history files of
a particular job. If nothing is specified, the logs are stored in
output directory. The files are stored in "_logs/history/" in the directory.
User can stop logging by giving the value "none".
mapreduce.jobtracker.jobhistory.completed.location The completed job history files are stored at this single well
known location. If nothing is specified, the files are stored at
${mapreduce.jobtracker.jobhistory.location}/done.
mapreduce.job.committer.setup.cleanup.neededtrue true, if job needs job-setup and job-cleanup.
false, otherwise
mapreduce.task.io.sort.factor10The number of streams to merge at once while sorting
files. This determines the number of open file handles.mapreduce.task.io.sort.mb100The total amount of buffer memory to use while sorting
files, in megabytes. By default, gives each merge stream 1MB, which
should minimize seeks.mapreduce.map.sort.spill.percent0.80The soft limit in the serialization buffer. Once reached, a
thread will begin to spill the contents to disk in the background. Note that
collection will not block if this threshold is exceeded while a spill is
already in progress, so spills may be larger than this threshold when it is
set to less than .5mapreduce.jobtracker.addresslocalThe host and port that the MapReduce job tracker runs
at. If "local", then jobs are run in-process as a single map
and reduce task.
mapreduce.local.clientfactory.class.nameorg.apache.hadoop.mapred.LocalClientFactoryThis the client factory that is responsible for
creating local job runner clientmapreduce.jobtracker.http.address0.0.0.0:50030
The job tracker http server address and port the server will listen on.
If the port is 0 then the server will start on a free port.
mapreduce.jobtracker.handler.count10
The number of server threads for the JobTracker. This should be roughly
4% of the number of tasktracker nodes.
mapreduce.tasktracker.report.address127.0.0.1:0The interface and port that task tracker server listens on.
Since it is only connected to by the tasks, it uses the local interface.
EXPERT ONLY. Should only be changed if your host does not have the loopback
interface.mapreduce.cluster.local.dir${hadoop.tmp.dir}/mapred/localThe local directory where MapReduce stores intermediate
data files. May be a comma-separated list of
directories on different devices in order to spread disk i/o.
Directories that do not exist are ignored.
mapreduce.jobtracker.system.dir${hadoop.tmp.dir}/mapred/systemThe directory where MapReduce stores control files.
mapreduce.jobtracker.staging.root.dir${hadoop.tmp.dir}/mapred/stagingThe root of the staging area for users' job files
In practice, this should be the directory where users' home
directories are located (usually /user)
mapreduce.cluster.temp.dir${hadoop.tmp.dir}/mapred/tempA shared directory for temporary files.
mapreduce.tasktracker.local.dir.minspacestart0If the space in mapreduce.cluster.local.dir drops under this,
do not ask for more tasks.
Value in bytes.
mapreduce.tasktracker.local.dir.minspacekill0If the space in mapreduce.cluster.local.dir drops under this,
do not ask more tasks until all the current ones have finished and
cleaned up. Also, to save the rest of the tasks we have running,
kill one of them, to clean up some space. Start with the reduce tasks,
then go with the ones that have finished the least.
Value in bytes.
mapreduce.jobtracker.expire.trackers.interval600000Expert: The time-interval, in miliseconds, after which
a tasktracker is declared 'lost' if it doesn't send heartbeats.
mapreduce.tasktracker.instrumentationorg.apache.hadoop.mapred.TaskTrackerMetricsInstExpert: The instrumentation class to associate with each TaskTracker.
mapreduce.tasktracker.resourcecalculatorplugin
Name of the class whose instance will be used to query resource information
on the tasktracker.
The class must be an instance of
org.apache.hadoop.util.ResourceCalculatorPlugin. If the value is null, the
tasktracker attempts to use a class appropriate to the platform.
Currently, the only platform supported is Linux.
mapreduce.tasktracker.taskmemorymanager.monitoringinterval5000The interval, in milliseconds, for which the tasktracker waits
between two cycles of monitoring its tasks' memory usage. Used only if
tasks' memory management is enabled via mapred.tasktracker.tasks.maxmemory.
mapreduce.tasktracker.tasks.sleeptimebeforesigkill5000The time, in milliseconds, the tasktracker waits for sending a
SIGKILL to a task, after it has been sent a SIGTERM. This is currently
not used on WINDOWS where tasks are just sent a SIGTERM.
mapreduce.job.maps2The default number of map tasks per job.
Ignored when mapreduce.jobtracker.address is "local".
mapreduce.job.reduces1The default number of reduce tasks per job. Typically set to 99%
of the cluster's reduce capacity, so that if a node fails the reduces can
still be executed in a single wave.
Ignored when mapreduce.jobtracker.address is "local".
mapreduce.jobtracker.restart.recoverfalse"true" to enable (job) recovery upon restart,
"false" to start afresh
mapreduce.jobtracker.jobhistory.block.size3145728The block size of the job history file. Since the job recovery
uses job history, its important to dump job history to disk as
soon as possible. Note that this is an expert level parameter.
The default value is set to 3 MB.
mapreduce.jobtracker.taskschedulerorg.apache.hadoop.mapred.JobQueueTaskSchedulerThe class responsible for scheduling the tasks.mapreduce.job.split.metainfo.maxsize10000000The maximum permissible size of the split metainfo file.
The JobTracker won't attempt to read split metainfo files bigger than
the configured value.
No limits if set to -1.
mapreduce.jobtracker.taskscheduler.maxrunningtasks.perjobThe maximum number of running tasks for a job before
it gets preempted. No limits if undefined.
mapreduce.map.maxattempts4Expert: The maximum number of attempts per map task.
In other words, framework will try to execute a map task these many number
of times before giving up on it.
mapreduce.reduce.maxattempts4Expert: The maximum number of attempts per reduce task.
In other words, framework will try to execute a reduce task these many number
of times before giving up on it.
mapreduce.reduce.shuffle.retry-delay.max.ms60000The maximum number of ms the reducer will delay before retrying
to download map data.
mapreduce.reduce.shuffle.parallelcopies5The default number of parallel transfers run by reduce
during the copy(shuffle) phase.
mapreduce.reduce.shuffle.connect.timeout180000Expert: The maximum amount of time (in milli seconds) reduce
task spends in trying to connect to a tasktracker for getting map output.
mapreduce.reduce.shuffle.read.timeout180000Expert: The maximum amount of time (in milli seconds) reduce
task waits for map output data to be available for reading after obtaining
connection.
mapreduce.task.timeout600000The number of milliseconds before a task will be
terminated if it neither reads an input, writes an output, nor
updates its status string. A value of 0 disables the timeout.
mapreduce.tasktracker.map.tasks.maximum2The maximum number of map tasks that will be run
simultaneously by a task tracker.
mapreduce.tasktracker.reduce.tasks.maximum2The maximum number of reduce tasks that will be run
simultaneously by a task tracker.
mapreduce.jobtracker.retiredjobs.cache.size1000The number of retired job status to keep in the cache.
mapreduce.tasktracker.outofband.heartbeatfalseExpert: Set this to true to let the tasktracker send an
out-of-band heartbeat on task-completion for better latency.
mapreduce.jobtracker.jobhistory.lru.cache.size5The number of job history files loaded in memory. The jobs are
loaded when they are first accessed. The cache is cleared based on LRU.
mapreduce.jobtracker.instrumentationorg.apache.hadoop.mapred.JobTrackerMetricsInstExpert: The instrumentation class to associate with each JobTracker.
mapred.child.java.opts-Xmx200mJava opts for the task tracker child processes.
The following symbol, if present, will be interpolated: @taskid@ is replaced
by current TaskID. Any other occurrences of '@' will go unchanged.
For example, to enable verbose gc logging to a file named for the taskid in
/tmp and to set the heap maximum to be a gigabyte, pass a 'value' of:
-Xmx1024m -verbose:gc -Xloggc:/tmp/@taskid@.gc
Usage of -Djava.library.path can cause programs to no longer function if
hadoop native libraries are used. These values should instead be set as part
of LD_LIBRARY_PATH in the map / reduce JVM env using the mapreduce.map.env and
mapreduce.reduce.env config settings.
mapred.child.envUser added environment variables for the task tracker child
processes. Example :
1) A=foo This will set the env variable A to foo
2) B=$B:c This is inherit tasktracker's B env variable.
mapreduce.admin.user.envLD_LIBRARY_PATH=$HADOOP_COMMON_HOME/lib/nativeExpert: Additional execution environment entries for
map and reduce task processes. This is not an additive property.
You must preserve the original value if you want your map and
reduce tasks to have access to native libraries (compression, etc).
mapreduce.task.tmp.dir./tmp To set the value of tmp directory for map and reduce tasks.
If the value is an absolute path, it is directly assigned. Otherwise, it is
prepended with task's working directory. The java tasks are executed with
option -Djava.io.tmpdir='the absolute path of the tmp dir'. Pipes and
streaming are set with environment variable,
TMPDIR='the absolute path of the tmp dir'
mapreduce.map.log.levelINFOThe logging level for the map task. The allowed levels are:
OFF, FATAL, ERROR, WARN, INFO, DEBUG, TRACE and ALL.
mapreduce.reduce.log.levelINFOThe logging level for the reduce task. The allowed levels are:
OFF, FATAL, ERROR, WARN, INFO, DEBUG, TRACE and ALL.
mapreduce.reduce.merge.inmem.threshold1000The threshold, in terms of the number of files
for the in-memory merge process. When we accumulate threshold number of files
we initiate the in-memory merge and spill to disk. A value of 0 or less than
0 indicates we want to DON'T have any threshold and instead depend only on
the ramfs's memory consumption to trigger the merge.
mapreduce.reduce.shuffle.merge.percent0.66The usage threshold at which an in-memory merge will be
initiated, expressed as a percentage of the total memory allocated to
storing in-memory map outputs, as defined by
mapreduce.reduce.shuffle.input.buffer.percent.
mapreduce.reduce.shuffle.input.buffer.percent0.70The percentage of memory to be allocated from the maximum heap
size to storing map outputs during the shuffle.
mapreduce.reduce.input.buffer.percent0.0The percentage of memory- relative to the maximum heap size- to
retain map outputs during the reduce. When the shuffle is concluded, any
remaining map outputs in memory must consume less than this threshold before
the reduce can begin.
mapreduce.reduce.shuffle.memory.limit.percent0.25Expert: Maximum percentage of the in-memory limit that a
single shuffle can consumemapreduce.reduce.markreset.buffer.percent0.0The percentage of memory -relative to the maximum heap size- to
be used for caching values when using the mark-reset functionality.
mapreduce.map.speculativetrueIf true, then multiple instances of some map tasks
may be executed in parallel.mapreduce.reduce.speculativetrueIf true, then multiple instances of some reduce tasks
may be executed in parallel.mapreduce.job.speculative.speculativecap0.1The max percent (0-1) of running tasks that
can be speculatively re-executed at any time.mapreduce.job.speculative.slowtaskthreshold1.0The number of standard deviations by which a task's
ave progress-rates must be lower than the average of all running tasks'
for the task to be considered too slow.
mapreduce.job.speculative.slownodethreshold1.0The number of standard deviations by which a Task
Tracker's ave map and reduce progress-rates (finishTime-dispatchTime)
must be lower than the average of all successful map/reduce task's for
the TT to be considered too slow to give a speculative task to.
mapreduce.job.jvm.numtasks1How many tasks to run per jvm. If set to -1, there is
no limit.
mapreduce.job.ubertask.enablefalseWhether to enable the small-jobs "ubertask" optimization,
which runs "sufficiently small" jobs sequentially within a single JVM.
"Small" is defined by the following maxmaps, maxreduces, and maxbytes
settings. Users may override this value.
mapreduce.job.ubertask.maxmaps9Threshold for number of maps, beyond which job is considered
too big for the ubertasking optimization. Users may override this value,
but only downward.
mapreduce.job.ubertask.maxreduces1Threshold for number of reduces, beyond which job is considered
too big for the ubertasking optimization. CURRENTLY THE CODE CANNOT SUPPORT
MORE THAN ONE REDUCE and will ignore larger values. (Zero is a valid max,
however.) Users may override this value, but only downward.
mapreduce.job.ubertask.maxbytesThreshold for number of input bytes, beyond which job is
considered too big for the ubertasking optimization. If no value is
specified, dfs.block.size is used as a default. Be sure to specify a
default value in mapred-site.xml if the underlying filesystem is not HDFS.
Users may override this value, but only downward.
mapreduce.input.fileinputformat.split.minsize0The minimum size chunk that map input should be split
into. Note that some file formats may have minimum split sizes that
take priority over this setting.mapreduce.jobtracker.maxtasks.perjob-1The maximum number of tasks for a single job.
A value of -1 indicates that there is no maximum. mapreduce.client.submit.file.replication10The replication level for submitted job files. This
should be around the square root of the number of nodes.
mapreduce.tasktracker.dns.interfacedefaultThe name of the Network Interface from which a task
tracker should report its IP address.
mapreduce.tasktracker.dns.nameserverdefaultThe host name or IP address of the name server (DNS)
which a TaskTracker should use to determine the host name used by
the JobTracker for communication and display purposes.
mapreduce.tasktracker.http.threads40The number of worker threads that for the http server. This is
used for map output fetching
mapreduce.tasktracker.http.address0.0.0.0:50060
The task tracker http server address and port.
If the port is 0 then the server will start on a free port.
mapreduce.task.files.preserve.failedtasksfalseShould the files for failed tasks be kept. This should only be
used on jobs that are failing, because the storage is never
reclaimed. It also prevents the map outputs from being erased
from the reduce directory as they are consumed.mapreduce.output.fileoutputformat.compressfalseShould the job outputs be compressed?
mapreduce.output.fileoutputformat.compress.typeRECORDIf the job outputs are to compressed as SequenceFiles, how should
they be compressed? Should be one of NONE, RECORD or BLOCK.
mapreduce.output.fileoutputformat.compress.codecorg.apache.hadoop.io.compress.DefaultCodecIf the job outputs are compressed, how should they be compressed?
mapreduce.map.output.compressfalseShould the outputs of the maps be compressed before being
sent across the network. Uses SequenceFile compression.
mapreduce.map.output.compress.codecorg.apache.hadoop.io.compress.DefaultCodecIf the map outputs are compressed, how should they be
compressed?
map.sort.classorg.apache.hadoop.util.QuickSortThe default sort class for sorting keys.
mapreduce.task.userlog.limit.kb0The maximum size of user-logs of each task in KB. 0 disables the cap.
mapreduce.job.userlog.retain.hours24The maximum time, in hours, for which the user-logs are to be
retained after the job completion.
mapreduce.jobtracker.hosts.filenameNames a file that contains the list of nodes that may
connect to the jobtracker. If the value is empty, all hosts are
permitted.mapreduce.jobtracker.hosts.exclude.filenameNames a file that contains the list of hosts that
should be excluded by the jobtracker. If the value is empty, no
hosts are excluded.mapreduce.jobtracker.heartbeats.in.second100Expert: Approximate number of heart-beats that could arrive
at JobTracker in a second. Assuming each RPC can be processed
in 10msec, the default value is made 100 RPCs in a second.
mapreduce.jobtracker.tasktracker.maxblacklists4The number of blacklists for a taskTracker by various jobs
after which the task tracker could be blacklisted across
all jobs. The tracker will be given a tasks later
(after a day). The tracker will become a healthy
tracker after a restart.
mapreduce.job.maxtaskfailures.per.tracker3The number of task-failures on a tasktracker of a given job
after which new tasks of that job aren't assigned to it. It
MUST be less than mapreduce.map.maxattempts and
mapreduce.reduce.maxattempts otherwise the failed task will
never be tried on a different node.
mapreduce.client.output.filterFAILEDThe filter for controlling the output of the task's userlogs sent
to the console of the JobClient.
The permissible options are: NONE, KILLED, FAILED, SUCCEEDED and
ALL.
mapreduce.client.completion.pollinterval5000The interval (in milliseconds) between which the JobClient
polls the JobTracker for updates about job status. You may want to set this
to a lower value to make tests run faster on a single node system. Adjusting
this value in production may lead to unwanted client-server traffic.
mapreduce.client.progressmonitor.pollinterval1000The interval (in milliseconds) between which the JobClient
reports status to the console and checks for job completion. You may want to set this
to a lower value to make tests run faster on a single node system. Adjusting
this value in production may lead to unwanted client-server traffic.
mapreduce.jobtracker.persist.jobstatus.activetrueIndicates if persistency of job status information is
active or not.
mapreduce.jobtracker.persist.jobstatus.hours1The number of hours job status information is persisted in DFS.
The job status information will be available after it drops of the memory
queue and between jobtracker restarts. With a zero value the job status
information is not persisted at all in DFS.
mapreduce.jobtracker.persist.jobstatus.dir/jobtracker/jobsInfoThe directory where the job status information is persisted
in a file system to be available after it drops of the memory queue and
between jobtracker restarts.
mapreduce.task.profilefalseTo set whether the system should collect profiler
information for some of the tasks in this job? The information is stored
in the user log directory. The value is "true" if task profiling
is enabled.mapreduce.task.profile.maps0-2 To set the ranges of map tasks to profile.
mapreduce.task.profile has to be set to true for the value to be accounted.
mapreduce.task.profile.reduces0-2 To set the ranges of reduce tasks to profile.
mapreduce.task.profile has to be set to true for the value to be accounted.
mapreduce.task.skip.start.attempts2 The number of Task attempts AFTER which skip mode
will be kicked off. When skip mode is kicked off, the
tasks reports the range of records which it will process
next, to the TaskTracker. So that on failures, TT knows which
ones are possibly the bad records. On further executions,
those are skipped.
mapreduce.map.skip.proc.count.autoincrtrue The flag which if set to true,
SkipBadRecords.COUNTER_MAP_PROCESSED_RECORDS is incremented
by MapRunner after invoking the map function. This value must be set to
false for applications which process the records asynchronously
or buffer the input records. For example streaming.
In such cases applications should increment this counter on their own.
mapreduce.reduce.skip.proc.count.autoincrtrue The flag which if set to true,
SkipBadRecords.COUNTER_REDUCE_PROCESSED_GROUPS is incremented
by framework after invoking the reduce function. This value must be set to
false for applications which process the records asynchronously
or buffer the input records. For example streaming.
In such cases applications should increment this counter on their own.
mapreduce.job.skip.outdir If no value is specified here, the skipped records are
written to the output directory at _logs/skip.
User can stop writing skipped records by giving the value "none".
mapreduce.map.skip.maxrecords0 The number of acceptable skip records surrounding the bad
record PER bad record in mapper. The number includes the bad record as well.
To turn the feature of detection/skipping of bad records off, set the
value to 0.
The framework tries to narrow down the skipped range by retrying
until this threshold is met OR all attempts get exhausted for this task.
Set the value to Long.MAX_VALUE to indicate that framework need not try to
narrow down. Whatever records(depends on application) get skipped are
acceptable.
mapreduce.reduce.skip.maxgroups0 The number of acceptable skip groups surrounding the bad
group PER bad group in reducer. The number includes the bad group as well.
To turn the feature of detection/skipping of bad groups off, set the
value to 0.
The framework tries to narrow down the skipped range by retrying
until this threshold is met OR all attempts get exhausted for this task.
Set the value to Long.MAX_VALUE to indicate that framework need not try to
narrow down. Whatever groups(depends on application) get skipped are
acceptable.
mapreduce.jobtracker.taskcache.levels2 This is the max level of the task cache. For example, if
the level is 2, the tasks cached are at the host level and at the rack
level.
mapreduce.job.queuenamedefault Queue to which a job is submitted. This must match one of the
queues defined in mapred-queues.xml for the system. Also, the ACL setup
for the queue must allow the current user to submit a job to the queue.
Before specifying a queue, ensure that the system is configured with
the queue, and access is allowed for submitting jobs to the queue.
mapreduce.cluster.acls.enabledfalse Specifies whether ACLs should be checked
for authorization of users for doing various queue and job level operations.
ACLs are disabled by default. If enabled, access control checks are made by
JobTracker and TaskTracker when requests are made by users for queue
operations like submit job to a queue and kill a job in the queue and job
operations like viewing the job-details (See mapreduce.job.acl-view-job)
or for modifying the job (See mapreduce.job.acl-modify-job) using
Map/Reduce APIs, RPCs or via the console and web user interfaces.
For enabling this flag(mapreduce.cluster.acls.enabled), this is to be set
to true in mapred-site.xml on JobTracker node and on all TaskTracker nodes.
mapreduce.job.acl-modify-job Job specific access-control list for 'modifying' the job. It
is only used if authorization is enabled in Map/Reduce by setting the
configuration property mapreduce.cluster.acls.enabled to true.
This specifies the list of users and/or groups who can do modification
operations on the job. For specifying a list of users and groups the
format to use is "user1,user2 group1,group". If set to '*', it allows all
users/groups to modify this job. If set to ' '(i.e. space), it allows
none. This configuration is used to guard all the modifications with respect
to this job and takes care of all the following operations:
o killing this job
o killing a task of this job, failing a task of this job
o setting the priority of this job
Each of these operations are also protected by the per-queue level ACL
"acl-administer-jobs" configured via mapred-queues.xml. So a caller should
have the authorization to satisfy either the queue-level ACL or the
job-level ACL.
Irrespective of this ACL configuration, (a) job-owner, (b) the user who
started the cluster, (c) members of an admin configured supergroup
configured via mapreduce.cluster.permissions.supergroup and (d) queue
administrators of the queue to which this job was submitted to configured
via acl-administer-jobs for the specific queue in mapred-queues.xml can
do all the modification operations on a job.
By default, nobody else besides job-owner, the user who started the cluster,
members of supergroup and queue administrators can perform modification
operations on a job.
mapreduce.job.acl-view-job Job specific access-control list for 'viewing' the job. It is
only used if authorization is enabled in Map/Reduce by setting the
configuration property mapreduce.cluster.acls.enabled to true.
This specifies the list of users and/or groups who can view private details
about the job. For specifying a list of users and groups the
format to use is "user1,user2 group1,group". If set to '*', it allows all
users/groups to modify this job. If set to ' '(i.e. space), it allows
none. This configuration is used to guard some of the job-views and at
present only protects APIs that can return possibly sensitive information
of the job-owner like
o job-level counters
o task-level counters
o tasks' diagnostic information
o task-logs displayed on the TaskTracker web-UI and
o job.xml showed by the JobTracker's web-UI
Every other piece of information of jobs is still accessible by any other
user, for e.g., JobStatus, JobProfile, list of jobs in the queue, etc.
Irrespective of this ACL configuration, (a) job-owner, (b) the user who
started the cluster, (c) members of an admin configured supergroup
configured via mapreduce.cluster.permissions.supergroup and (d) queue
administrators of the queue to which this job was submitted to configured
via acl-administer-jobs for the specific queue in mapred-queues.xml can
do all the view operations on a job.
By default, nobody else besides job-owner, the user who started the
cluster, memebers of supergroup and queue administrators can perform
view operations on a job.
mapreduce.tasktracker.indexcache.mb10 The maximum memory that a task tracker allows for the
index cache that is used when serving map outputs to reducers.
mapreduce.task.merge.progress.records10000 The number of records to process during merge before
sending a progress notification to the TaskTracker.
mapreduce.job.reduce.slowstart.completedmaps0.05Fraction of the number of maps in the job which should be
complete before reduces are scheduled for the job.
mapreduce.job.complete.cancel.delegation.tokenstrue if false - do not unregister/cancel delegation tokens from
renewal, because same tokens may be used by spawned jobs
mapreduce.tasktracker.taskcontrollerorg.apache.hadoop.mapred.DefaultTaskControllerTaskController which is used to launch and manage task execution
mapreduce.tasktracker.groupExpert: Group to which TaskTracker belongs. If
LinuxTaskController is configured via mapreduce.tasktracker.taskcontroller,
the group owner of the task-controller binary should be same as this group.
mapreduce.tasktracker.healthchecker.script.pathAbsolute path to the script which is
periodicallyrun by the node health monitoring service to determine if
the node is healthy or not. If the value of this key is empty or the
file does not exist in the location configured here, the node health
monitoring service is not started.mapreduce.tasktracker.healthchecker.interval60000Frequency of the node health script to be run,
in millisecondsmapreduce.tasktracker.healthchecker.script.timeout600000Time after node health script should be killed if
unresponsive and considered that the script has failed.mapreduce.tasktracker.healthchecker.script.argsList of arguments which are to be passed to
node health script when it is being launched comma seperated.
mapreduce.job.counters.limit120Limit on the number of user counters allowed per job.
mapreduce.framework.namelocalThe runtime framework for executing MapReduce jobs.
Can be one of local, classic or yarn.
yarn.app.mapreduce.am.staging-dir/tmp/hadoop-yarn/stagingThe staging dir used while submitting jobs.
mapreduce.job.end-notification.urlIndicates url which will be called on completion of job to inform
end status of job.
User can give at most 2 variables with URI : $jobId and $jobStatus.
If they are present in URI, then they will be replaced by their
respective values.
mapreduce.job.end-notification.retry.attempts0The number of times the submitter of the job wants to retry job
end notification if it fails. This is capped by
mapreduce.job.end-notification.max.attemptsmapreduce.job.end-notification.retry.interval1000The number of milliseconds the submitter of the job wants to
wait before job end notification is retried if it fails. This is capped by
mapreduce.job.end-notification.max.retry.intervalmapreduce.job.end-notification.max.attempts5trueThe maximum number of times a URL will be read for providing job
end notification. Cluster administrators can set this to limit how long
after end of a job, the Application Master waits before exiting. Must be
marked as final to prevent users from overriding this.
mapreduce.job.end-notification.max.retry.interval5000trueThe maximum amount of time (in milliseconds) to wait before
retrying job end notification. Cluster administrators can set this to
limit how long the Application Master waits before exiting. Must be marked
as final to prevent users from overriding this.yarn.app.mapreduce.am.envUser added environment variables for the MR App Master
processes. Example :
1) A=foo This will set the env variable A to foo
2) B=$B:c This is inherit tasktracker's B env variable.
yarn.app.mapreduce.am.command-opts-Xmx1024mJava opts for the MR App Master processes.
The following symbol, if present, will be interpolated: @taskid@ is replaced
by current TaskID. Any other occurrences of '@' will go unchanged.
For example, to enable verbose gc logging to a file named for the taskid in
/tmp and to set the heap maximum to be a gigabyte, pass a 'value' of:
-Xmx1024m -verbose:gc -Xloggc:/tmp/@taskid@.gc
Usage of -Djava.library.path can cause programs to no longer function if
hadoop native libraries are used. These values should instead be set as part
of LD_LIBRARY_PATH in the map / reduce JVM env using the mapreduce.map.env and
mapreduce.reduce.env config settings.
yarn.app.mapreduce.am.admin-command-optsJava opts for the MR App Master processes for admin purposes.
It will appears before the opts set by yarn.app.mapreduce.am.command-opts and
thus its options can be overridden user.
Usage of -Djava.library.path can cause programs to no longer function if
hadoop native libraries are used. These values should instead be set as part
of LD_LIBRARY_PATH in the map / reduce JVM env using the mapreduce.map.env and
mapreduce.reduce.env config settings.
yarn.app.mapreduce.am.job.task.listener.thread-count30The number of threads used to handle RPC calls in the
MR AppMaster from remote tasksyarn.app.mapreduce.am.job.client.port-rangeRange of ports that the MapReduce AM can use when binding.
Leave blank if you want all possible ports.
For example 50000-50050,50100-50200yarn.app.mapreduce.am.job.committer.cancel-timeout60000The amount of time in milliseconds to wait for the output
committer to cancel an operation if the job is killedyarn.app.mapreduce.am.job.committer.commit-window10000Defines a time window in milliseconds for output commit
operations. If contact with the RM has occurred within this window then
commits are allowed, otherwise the AM will not allow output commits until
contact with the RM has been re-established.yarn.app.mapreduce.am.scheduler.heartbeat.interval-ms1000The interval in ms at which the MR AppMaster should send
heartbeats to the ResourceManageryarn.app.mapreduce.client-am.ipc.max-retries1The number of client retries to the AM - before reconnecting
to the RM to fetch Application Status.yarn.app.mapreduce.client.max-retries3The number of client retries to the RM/HS/AM before
throwing exception. This is a layer above the ipc.yarn.app.mapreduce.am.resource.mb1536The amount of memory the MR AppMaster needs.mapreduce.jobhistory.address0.0.0.0:10020MapReduce JobHistory Server IPC host:portmapreduce.jobhistory.webapp.address0.0.0.0:19888MapReduce JobHistory Server Web UI host:portmapreduce.jobhistory.keytab
Location of the kerberos keytab file for the MapReduce
JobHistory Server.
/etc/security/keytab/jhs.service.keytabmapreduce.jobhistory.principal
Kerberos principal name for the MapReduce JobHistory Server.
jhs/_HOST@REALM.TLD